Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement

• URL: http://arxiv.org/abs/2506.04753v1
• Date: Thu, 05 Jun 2025 08:39:17 GMT
• Title: Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement
• Authors: Niki Martinel, Rita Pucci,
• Abstract summary: We present a novel dual-stream architecture that achieves state-of-the-art underwater image enhancement.<n>Our method simultaneously estimates transmission maps and spatially-varying background light through a dedicated physics estimator.<n>Our approach also features a novel optimization objective ensuring both physical adherence and perceptual quality across multiple spatial frequencies.
• Score: 8.16306466526838
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a novel dual-stream architecture that achieves state-of-the-art underwater image enhancement by explicitly integrating the Jaffe-McGlamery physical model with capsule clustering-based feature representation learning. Our method simultaneously estimates transmission maps and spatially-varying background light through a dedicated physics estimator while extracting entity-level features via capsule clustering in a parallel stream. This physics-guided approach enables parameter-free enhancement that respects underwater formation constraints while preserving semantic structures and fine-grained details. Our approach also features a novel optimization objective ensuring both physical adherence and perceptual quality across multiple spatial frequencies. To validate our approach, we conducted extensive experiments across six challenging benchmarks. Results demonstrate consistent improvements of $+0.5$dB PSNR over the best existing methods while requiring only one-third of their computational complexity (FLOPs), or alternatively, more than $+1$dB PSNR improvement when compared to methods with similar computational budgets. Code and data \textit{will} be available at https://github.com/iN1k1/.

Related papers

• Layer-wise Quantization for Quantized Optimistic Dual Averaging [75.4148236967503]
  We develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training.<n>We propose a novel Quantized Optimistic Dual Averaging (QODA) algorithm with adaptive learning rates, which achieves competitive convergence rates for monotone VIs.
  arXiv  Detail & Related papers  (2025-05-20T13:53:58Z)
• A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils [61.60175086194333]
  aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils.<n>Here, we consider modeling of incompressible flows over solid objects, wherein geometric structures are a key factor in determining aerodynamics.<n>To effectively incorporate geometries, we propose a message passing scheme that efficiently and expressively integrates the airfoil shape with the mesh representation.<n>These design choices lead to a purely data-driven machine learning framework known as GeoMPNN, which won the Best Student Submission award at the NeurIPS 2024 ML4CFD Competition, placing 4th overall.
  arXiv  Detail & Related papers  (2024-12-12T16:05:39Z)
• ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts [71.91042186338163]
  ALoRE is a novel PETL method that reuses the hypercomplex parameterized space constructed by Kronecker product to Aggregate Low Rank Experts.<n>Thanks to the artful design, ALoRE maintains negligible extra parameters and can be effortlessly merged into the frozen backbone.
  arXiv  Detail & Related papers  (2024-12-11T12:31:30Z)
• Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstruction [8.598010350935596]
  This paper systematically evaluates a deep learning reconstruction framework.<n>We design a hybrid loss function combining weighted mean squared with error structural similarity index.<n>We enhance the model's capability to capture cross-modaltemporal correlations and energy-displacement nonlinearities.
  arXiv  Detail & Related papers  (2024-10-08T11:49:18Z)
• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• DualFluidNet: an Attention-based Dual-pipeline Network for FLuid Simulation [4.694954114339147]
  We propose an innovative approach for 3D fluid simulations utilizing an Attention-based Dual-pipeline Network. We find a way to achieve a better balance between global fluid control and physical law constraints. We also propose a new dataset, Tank3D, to further explore the network's ability to handle more complicated scenes.
  arXiv  Detail & Related papers  (2023-12-28T07:37:11Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Variational Bayes image restoration with compressive autoencoders [4.879530644978008]
  Regularization of inverse problems is of paramount importance in computational imaging. In this work, we first propose to use compressive autoencoders instead of state-of-the-art generative models. As a second contribution, we introduce the Variational Bayes Latent Estimation (VBLE) algorithm.
  arXiv  Detail & Related papers  (2023-11-29T15:49:31Z)
• An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecture [0.0]
  This work presents a two-stage adaptive framework for developing deep neural network (DNN) architectures that generalize well for a given training data set. In the first stage, a layerwise training approach is adopted where a new layer is added each time and trained independently by freezing parameters in the previous layers. We introduce a epsilon-delta stability-promoting concept as a desirable property for a learning algorithm and show that employing manifold regularization yields a epsilon-delta stability-promoting algorithm.
  arXiv  Detail & Related papers  (2022-11-13T09:51:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.